Frames, claims and audiences: Construction of food allergies in the Canadian media
Bibliographic record
Abstract
Food allergies are newly emerging health risks, and some evidence indicates that their prevalence is increasing. Public perception, however, is that the prevalence of food allergies is much greater than systematic estimates suggest. As food allergies increasingly permeate everyday life, this paper explores how associated risks are constructed through the mass media. In particular, nine years of media coverage of food allergies are analysed through the lens of issue framing and claims-making. Results show that advocates and affected individuals dominate discussions around policy action, while researchers and health professionals are diagnosing the causes of food allergy. Results also suggest that there is competition over the definition of food allergies, which may, in turn, be shaping public understanding of the related risks. There is also an indication that the framing of food allergies is evolving over time, and that the discussion is becoming increasingly one-sided with affected individuals leading the charge.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.012 | 0.008 |
| Science and technology studies | 0.020 | 0.015 |
| Scholarly communication | 0.019 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".